Embodiments of the present invention relate generally to diagnostic imaging and, more particularly, to an apparatus and method for multi-energy tissue quantification of one or more tissues-of-interest.
Medical imaging devices comprise x-ray systems, magnetic resonance (MR) systems, ultrasound systems, computed tomography (CT) systems, positron emission tomography (PET) systems, ultrasound, nuclear medicine, and other types of imaging systems. Typically, in CT imaging systems, an x-ray source emits a fan-shaped beam toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” shall include anything capable of being imaged. The beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject. Each detector element of the detector array produces a separate electrical signal indicative of the attenuated beam received by each detector element. The electrical signals are transmitted to a data processing system for analysis which ultimately produces an image.
Generally, the x-ray source and the detector array are rotated about the gantry opening within an imaging plane and around the subject. X-ray sources typically include x-ray tubes, which emit the x-ray beam at a focal point. X-ray detectors typically include a collimator for collimating x-ray beams received at the detector, a scintillator for converting x-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom.
Typically, each scintillator of a scintillator array converts x-rays to light energy. Each scintillator discharges light energy to a photodiode adjacent thereto. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are then transmitted to the data processing system for image reconstruction. Such typical systems, however, do not include an ability to discriminate spectral energy content of x-rays as they pass through an object being imaged.
However, as known in the art, multi-energy spectral CT systems have been developed that can reveal the densities of different materials in an object and generate images acquired at multiple monochromatic x-ray energy levels. In the absence of object scatter, a system derives the behavior at a different energy based on a signal from two regions of photon energy in the spectrum: the low-energy and the high-energy portions of the incident x-ray spectrum. Different approaches have been developed to realize dual energy or spectral imaging. To name a few, dual x-ray source and detector, a single x-ray source with an energy discriminative detector, and a single x-ray source and detector with multiple acquisitions at different kVp or interleaved with fast kVp switching capability are examples of techniques.
In a dual x-ray source and detector system, typically two x-ray sources are provided, each having a respective detector positioned opposite thereto such that x-rays may be emitted from each source having a different spectral energy content. Thus, based on the known energy difference of the sources, a scintillating or energy integrating device may suffice to distinguish energy content and different materials within the object being imaged.
In a single x-ray source with an energy discriminative detector, energy sensitive detectors may be used such that each x-ray photon reaching the detector is recorded with its photon energy. Such systems may use a direct conversion detector material in lieu of a scintillator.
Accurate quantification of liver fat is important in the diagnosis, characterization, and treatment of fatty liver disease. The early diagnosis of fatty liver disease with associated treatments therefor can help prevent the onset of more serious liver diseases and can even lead to a reverse of some forms of fatty liver disease. Furthermore, concentration of liver fat can be used as a clinical diagnostic for liver resection.
Liver biopsy is a technique used for liver-fat quantification, but biopsy results may be subject to dispute due to sampling error. In addition, a liver biopsy requires an invasive procedure to obtain a tissue sample on which to perform the biopsy.
Non-invasive imaging with magnetic resonance (MR), ultrasound (US), and computed tomography (CT) scanners are of increasing interest and acceptance. Of these three modalities, MR is most often cited as the superior modality for liver-fat quantification since it offers several methods for direct and accurate liver-fat quantification. However, alternatives to MR are desired since MR scanning is costly and time-consuming.
Many techniques currently exist for the quantification of liver fat with CT, and all rely on the inverse relationship between liver fat content and liver attenuation. A first technique involves direct measurement of liver attenuation of values in Hounsfield units (HU). Second and third techniques normalize the average HU value of the liver by that of the spleen, and either involve computing differences (liver minus spleen) or ratios (typically, spleen to liver). However, these techniques are semi-quantitative and infer concentration of liver fat heuristically.
One limitation of these CT techniques is that they are impractical for iodinated contrast-enhanced CT acquisitions, where the presence of iodinated contrast agents greatly skews HU values. This variability depends on which contrast agent was administered, patient-specific absorption rates of contrast media, and contrast-enhanced phase of imaging (arterial, portal venous, delayed). Furthermore, HU values of the liver and spleen are often obtained through the use of 2D regions of interest (ROIs), which may lead to placement and registration error, and may not be representative of the whole liver. While dual-energy CT (DECT) may overcome a contrast-enhanced limitation via material decomposition, existing methods still rely on HU values and thus remain semi-quantitative in nature.
Therefore, it would be desirable to have a system and method of fat quantification that overcome the aforementioned drawbacks.